Multi-Agent System (MAS) Architecture

• Multi-Agent System (MAS) Architecture is a framework that defines distributed autonomous agents, employing clear roles, communication protocols, and scalable hierarchies to achieve both local and global objectives.
• It integrates specialized components such as VEN-based agents, digital twins, and sentinel agents to optimize supply chain management, automate workflows, and bolster system security.
• The architecture promotes decentralized decision-making, adaptive scheduling, and modular expansion, enabling its application in diverse domains including scientific workflows and LLM-driven collaborative intelligence.

A Multi-Agent System (MAS) Architecture defines the organizational, functional, and interactional structures that govern distributed autonomous agents working collectively to achieve local and global objectives. MAS architectures can span domains as diverse as supply chain optimization, scientific workflow automation, runtime safety in cyber-physical systems, and advanced LLM-driven collaborative intelligence. Distinct architectural paradigm choices—including agent role specialization, communication protocols, organizational hierarchies, and mechanisms for autonomy and coordination—determine system flexibility, scalability, robustness, and application alignment.

1. Core Structural Patterns and Agent Role Specialization

Multi-agent system architectures typically formalize agent organizations through tiered, layered, or networked constructs, assigning agents clearly delineated roles and responsibilities:

• Virtual Enterprise Node (VEN)-based Architectures: In multi-site industrial contexts, each enterprise or cluster forms a VEN—an autonomous decision center responsible for both local operations (internal planning, execution) and external interactions (negotiation, coordination). Each VEN contains a Negotiator Agent (for handling external messaging and contract negotiation) and a Planner Agent (for production or resource allocation decisions) (0806.3031). These roles are further complemented by escalation-level agents, e.g., Tier Negotiator Agent (TNA) and Supply Chain Mediator Agent (SCMA), that resolve perturbations and network-level constraints only when decentralized mechanisms fail.
• Hierarchical and Layered Organizations: Some frameworks, such as HEnRY, implement a tree-like hierarchy: Digital Twin agents act as principals on behalf of humans, Facilitator agents manage task decomposition across multiple domains, Domain Agents handle specific knowledge/workflows, and ephemeral Mediator agents manage asynchronous collaboration (e.g., “agora” environments for converging on solutions) (Lacavalla et al., 16 Oct 2024).
• Network-centric and Service-oriented Models: Architectures like AaaS-AN instantiate both individual agents and agent groups as dynamic network vertices. The Role-Goal-Process-Service (RGPS) meta-model defines each agent/group by richly attributed role, goal, process, and service parameters (A = {name, description, system prompt, input, output, code}), supporting compositional, plug-and-play orchestration over an execution graph (Zhu et al., 13 May 2025).
• Functional Specialization for Automated Workflows: Automated scientific workflows decompose analysis into RAG agents (for retrieval), coder agents (code generation/execution), and manager/planner agents (subtask orchestration) (Laverick et al., 30 Nov 2024). In collaborative clinical decision systems, a Manager dynamically assigns medical specialties (as specialist agents) who collaborate in an iterative, hierarchical debate loop (Lee et al., 29 Aug 2025).
• Advanced Roles in Safety and Defense: Security-centric architectures deploy Sentinel Agents distributed across the communication fabric to monitor and semantically analyze messages, using LLM-based, behavioral, and rule-based detectors, with escalation to a Coordinator Agent for policy enforcement, agent quarantine, and audit trail governance (Gosmar et al., 18 Sep 2025).

2. Coordination, Autonomy, and Decision-Making Mechanisms

Decentralized control and distributed decision-making are intrinsic to robust MAS design, but practical coordination is achieved through architected protocols, escalation levels, and role-based behaviors:

• Decentralized, Heterarchical Control: In heterarchical organizational models (e.g., supply chain MAS (0806.3031)), each VEN or agent node independently negotiates with immediate neighbors (upstream, downstream), exchanging scenario-specific messages (e.g., C_US, RN_US, N_DS) while adhering to shared negotiation protocols and escalation paths (to TNA/SCMA) for consistency and network-wide objectives.
• Escalation and Conflict Resolution: Problems unresolved at the agent or tier level trigger activation of higher-order agents—TNA for tier-level congestion, SCMA or Mediator for global tradeoff balancing (e.g., maximizing Z = total selling prices – total costs subject to Z ≥ 0)—ensuring local deficits can be absorbed for global optimality (0806.3031).
• Task Allocation and Monitoring: Adaptive architectures (e.g., DRAMA (Wang et al., 6 Aug 2025)) abstract both agents and tasks as resource objects with dynamic attributes. Allocation uses affinity-driven, loosely coupled schedulers (fₜ = Scheduler(𝓧₍rₖ, t₎, ℰₜ)), and real-time monitoring aggregates attribute sets for centralized re-planning, while the worker plane agents collaborate and perform local reasoning, task decomposition, and takeover when failures occur.
• Step-level Policy Autonomy: Allen delineates a four-tier state model (Task, Stage, Agent, Step) and redefines the execution unit to “Step”—the atomic action—enabling agents to dynamically assemble, plan, and adapt behavior beyond static workflow branches. Policy autonomy is achieved through the ability to adjust step sequences, tools, and collaborative configurations at runtime, with hierarchical states providing structured oversight (Zhou et al., 15 Aug 2025).
• Collaborative and Consensus Mechanisms: MAS used in clinical settings (Lee et al., 29 Aug 2025) employ a Manager-driven, multi-round debate: each specialist agent presents independent analysis, and subsequent rounds enable argument-driven consensus formation (requiring, e.g., >80% agreement), with team reassignment or managerial override in case of deadlock.

3. Communication Protocols and Interoperability

MAS architectures depend on semantically rich, context-aware protocol stacks for efficiency, interoperability, and scalability:

Protocol	Function	Application Contexts
Agent Communication Language (ACL)	Standardizes message intent (e.g., request, inform) and semantics	Microservices MAS (Goyal et al., 5 May 2025)
Model Context Protocol (MCP)	Client-server interface for sharing external tools/data context	Microservices/Plug-and-Play MAS (Goyal et al., 5 May 2025, Zhu et al., 13 May 2025)
Application-to-Application (A2A)	Direct agent coordination, task and capability advertisement	Plug-and-Play, Semi-centralized MAS (Goyal et al., 5 May 2025, Ren et al., 23 Aug 2025)

Structured A2A communication environments (e.g., Anemoi (Ren et al., 23 Aug 2025)) expose primitives for agent discovery, thread creation, and directed messaging, enabling agents to monitor progress, suggest plan refinements, and collectively synthesize solutions—minimizing context-passing redundancy and scaling cost-efficiently even under resource-constrained planners.

4. Emergent Applications and Architectural Innovations

Recent MAS architectures demonstrate significant advances in agility, scalability, and application scope:

• LLM-driven and Heterogeneous Multi-Agent Systems: Systems like X-MAS assign specialized LLMs to agents based on function and domain, validated empirically through cross-domain benchmarks (e.g., MATH, AIME). Heterogeneous configurations outperform homogeneous ones by up to 47% absolute improvement in complex scenarios, supporting the “collective intelligence” paradigm (Ye et al., 22 May 2025). MAS-GPT proposes a generative approach: a single LLM generates executable, query-adaptive MAS code for each prompt, enhancing efficiency and generalization (Ye et al., 5 Mar 2025).
• Neural Orchestration and Data-driven Agent Selection: Neural orchestration frameworks (MetaOrch) utilize supervised learning over agent profiles, task semantic vectors, and soft (fuzzy) evaluation signals for dynamic, accuracy-optimized agent-task assignment. Experiments reveal up to 91.1% accuracy in optimal agent selection, with modular extensibility for new agents and domains (Agrawal et al., 3 May 2025).
• Security, Observability, and Regulatory Compliance: Sentinel Agents perform continuous, multi-layered inspection (rule-based and LLM-semantic filters, behavioral analytics), supported by Coordinator Agents for adaptive policy enforcement, agent quarantine, and audit trail publication. Detecting 100% of 162 injected attacks in simulation, this architecture underpins system-wide security, enhanced observability, and supports compliance with evolving standards (e.g., GDPR, HIPAA) (Gosmar et al., 18 Sep 2025).
• Service-oriented and Dataset-driven MAS: In AaaS-AN, agent groups and RPA workflows organize over 100 plug-and-play agent services within a dynamic, process-aware execution graph. A released dataset of 10,000 long-chain workflows enables robust benchmarking of long-horizon, collaborative reasoning and service orchestration (Zhu et al., 13 May 2025).

5. Flexibility, Scalability, and Adaptability

Key architectural traits underpinning MAS effectiveness include:

• Decentralization and Autonomy: Decoupled decision centers (VENs, agents-as-microservices) permit localized adaptation and update without global redeployment (0806.3031, Goyal et al., 5 May 2025). Each agent maintains bounded context, enhancing modularity and maintainability.
• Continuous Monitoring and Dynamic Scheduling: Modular divisions (control vs. worker plane) and centralized planners allow rapid response to agent turnover, context changes, and task reallocation. DRAMA’s real-time scheduler and step-level policy autonomy in Allen exemplify such flexibility (Wang et al., 6 Aug 2025, Zhou et al., 15 Aug 2025).
• Expandability and Integration: Reusable component meta-models (AUML/UML-based, as in (Maalal et al., 2012)) and clear service discovery protocols facilitate incorporation of new agent types, domains, or external tools with minimal re-engineering overhead.
• Robustness Under Dynamic Conditions: Robust MAS gracefully accommodate failures, dropouts, and unpredictable events through task takeover, dynamic workflow restructuring, and layered escalation pathways.

6. Challenges and Limitations

• Coordination Overhead: Increasing agent count and escalation levels can intensify negotiation complexity and message management (0806.3031, 0911.0912).
• Standardization and Interoperability: Efficient MAS operation across heterogeneous IT ecosystems requires adoption of standard communication protocols and agent meta-models.
• Groupthink and Fault Propagation: In clinical-consultation MAS, group decisioning processes may increase susceptibility to groupthink, which can be partially mitigated via iterative debate, team reassignment, and fallback mechanisms (Lee et al., 29 Aug 2025).
• Security and Compliance: Persistent monitoring is essential to prevent adversarial behaviors, hallucinations, and privacy breaches, mandating multi-layered security architectures (Gosmar et al., 18 Sep 2025).
• Integration Complexity and Real-Time Synchronization: Achieving distributed consistency and low-latency coordination remains challenging, especially as MAS deployments scale in size and application criticality.

7. Application Domains and Case Studies

MAS architectures have been validated in a range of applications:

Application Domain	MAS Structural Highlights	Key Paper(s)
Supply Chain & Manufacturing	VEN-based, negotiator/planner agents, escalation to TNA/SCMA	(0806.3031); (0806.3032); (0911.0912)
Scientific Workflow (Cosmology)	RAG, coder, manager agents, stepwise automation	(Laverick et al., 30 Nov 2024)
Software Architecture Automation	Sequential multi-agent collaboration (Analyst, Modeler, Designer, Evaluator), knowledge-driven evaluation	(Li et al., 28 Jul 2025)
Security & Trust in MAS	Sentinel Agents, Coordinator Agent, LLM-based semantic analysis	(Gosmar et al., 18 Sep 2025)
LLM-Based MAS Generation	Automated code-based MAS generation via unified interface	(Ye et al., 5 Mar 2025)
Heterogeneous LLM MAS	Agents powered by diverse LLMs, performance-driven assignment	(Ye et al., 22 May 2025)
Clinical Decision Support	Dynamic team assignment, consensus through iterative debate	(Lee et al., 29 Aug 2025)

This breadth of application, combined with rigorous validation, underscores the adaptability and growing sophistication of modern MAS architecture.